In [15]:
# Visualizando as correlações
plt.subplots(figsize=(24, 18))
sns.heatmap(final_df.corr(), annot=True)
plt.show()
import sys
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sys.path.append("./utils")
pd.options.plotting.backend = "plotly"
import loader
import round_classifier
import thresholds
from Parser import Parser
all_data = loader.load_dataset()
all_columns = all_data.columns
all_columns[0:16]
Index(['time_left', 'ct_score', 't_score', 'map', 'bomb_planted', 'ct_health',
't_health', 'ct_armor', 't_armor', 'ct_money', 't_money', 'ct_helmets',
't_helmets', 'ct_defuse_kits', 'ct_players_alive', 't_players_alive'],
dtype='object')
def assemble_df_weapons(_df, _weapons):
pre_df = {}
for i, weapon in enumerate(_weapons):
key = weapon.replace("ct_weapon_", "").replace("t_weapon_", "")
pre_df[key] = _df[weapon]
return pd.DataFrame(pre_df)
def assemble_df_grenades(_df, _grenades):
pre_df = {}
for i, grenade in enumerate(_grenades):
key = grenade.replace("ct_grenade_", "").replace("t_grenade_", "")
pre_df[key] = _df[grenade]
return pd.DataFrame(pre_df)
# Weapons Percentage
weapons = [
"ct_weapon_ak47",
"t_weapon_ak47",
"ct_weapon_aug",
"t_weapon_aug",
"ct_weapon_awp",
"t_weapon_awp",
"ct_weapon_bizon",
"t_weapon_bizon",
"ct_weapon_cz75auto",
"t_weapon_cz75auto",
"ct_weapon_elite",
"t_weapon_elite",
"ct_weapon_famas",
"t_weapon_famas",
"ct_weapon_g3sg1",
"t_weapon_g3sg1",
"ct_weapon_galilar",
"t_weapon_galilar",
"ct_weapon_glock",
"t_weapon_glock",
"ct_weapon_m249",
"t_weapon_m249",
"ct_weapon_m4a1s",
"t_weapon_m4a1s",
"ct_weapon_m4a4",
"t_weapon_m4a4",
"ct_weapon_mac10",
"t_weapon_mac10",
"ct_weapon_mag7",
"t_weapon_mag7",
"ct_weapon_mp5sd",
"t_weapon_mp5sd",
"ct_weapon_mp7",
"t_weapon_mp7",
"ct_weapon_mp9",
"t_weapon_mp9",
"ct_weapon_negev",
"t_weapon_negev",
"ct_weapon_nova",
"t_weapon_nova",
"ct_weapon_p90",
"t_weapon_p90",
"ct_weapon_r8revolver",
"t_weapon_r8revolver",
"ct_weapon_sawedoff",
"t_weapon_sawedoff",
"ct_weapon_scar20",
"t_weapon_scar20",
"ct_weapon_sg553",
"t_weapon_sg553",
"ct_weapon_ssg08",
"t_weapon_ssg08",
"ct_weapon_ump45",
"t_weapon_ump45",
"ct_weapon_xm1014",
"t_weapon_xm1014",
"ct_weapon_deagle",
"t_weapon_deagle",
"ct_weapon_fiveseven",
"t_weapon_fiveseven",
"ct_weapon_usps",
"t_weapon_usps",
"ct_weapon_p250",
"t_weapon_p250",
"ct_weapon_p2000",
"t_weapon_p2000",
"ct_weapon_tec9",
"t_weapon_tec9",
]
t_weapons = list(filter(lambda el: el[0:1] == "t", weapons))
display(
assemble_df_weapons(all_data, t_weapons)
.sum()
.sort_values(ascending=True)
.plot.barh(title="T Weapons")
)
ct_weapons = list(filter(lambda el: el[0:2] == "ct", weapons))
display(
assemble_df_weapons(all_data, ct_weapons)
.sum()
.sort_values(ascending=True)
.plot.barh(title="CT Weapons")
)
grenades = [
"ct_grenade_hegrenade",
"t_grenade_hegrenade",
"ct_grenade_flashbang",
"t_grenade_flashbang",
"ct_grenade_smokegrenade",
"t_grenade_smokegrenade",
"ct_grenade_incendiarygrenade",
"t_grenade_incendiarygrenade",
"ct_grenade_molotovgrenade",
"t_grenade_molotovgrenade",
"ct_grenade_decoygrenade",
"t_grenade_decoygrenade",
]
t_grenades = list(filter(lambda el: el[0:1] == "t", grenades))
display(
assemble_df_weapons(all_data, t_grenades)
.sum()
.sort_values(ascending=True)
.plot.barh(title="T Grenades")
)
ct_grenades = list(filter(lambda el: el[0:2] == "ct", grenades))
display(
assemble_df_weapons(all_data, ct_grenades)
.sum()
.sort_values(ascending=True)
.plot.barh(title="CT Grenades")
)
parser = Parser()
treat_data = parser.create_round_column(all_data.copy())
treat_data = parser.classify_weapons(treat_data.copy())
treat_data = parser.create_round_winner_columns(treat_data.copy())
treat_data["pistol_round"] = treat_data.apply(
round_classifier.define_pistol_round, axis=1
)
# Defining eco rounds
treat_data["ct_eco"] = treat_data.apply(round_classifier.define_full_ct_eco, axis=1)
treat_data["t_eco"] = treat_data.apply(round_classifier.define_full_t_eco, axis=1)
# Defining force buy rounds
treat_data["ct_force"] = treat_data.apply(
round_classifier.define_force_ct_round, axis=1
)
treat_data["t_force"] = treat_data.apply(round_classifier.define_force_t_round, axis=1)
# Não acho que são as colunas certas pra analisar, mas só um exemplo do que podemos fazer
n_bins = 7
lower, higher = thresholds.prepare(treat_data.copy(), "ct_health")
treat_data["ct_health_thresholds"] = thresholds.build(
treat_data.copy(), "ct_health", lower, higher, n_bins
)
treat_data["ct_health_thresholds_num"] = thresholds.build_numerical(
treat_data.copy(), "ct_health", lower, higher, n_bins
)
# Não acho que são as colunas certas pra analisar, mas só um exemplo do que podemos fazer
n_bins = 7
lower, higher = thresholds.prepare(treat_data.copy(), "t_health")
treat_data["t_health_thresholds"] = thresholds.build(
treat_data.copy(), "t_health", lower, higher, n_bins
)
treat_data["t_health_thresholds_num"] = thresholds.build_numerical(
treat_data.copy(), "t_health", lower, higher, n_bins
)
# Selecting columns
normal_columns = all_columns[0:16].tolist()
made_columns_t = [
"t_main_rifle",
"t_sec_rifle",
"t_force_weapons",
"t_weak_pistols",
"t_strong_pistols",
"t_granades",
"t_weapon_awp",
"t_eco",
"t_force",
]
made_columns_ct = [
"ct_main_rifle",
"ct_sec_rifle",
"ct_force_weapons",
"ct_weak_pistols",
"ct_strong_pistols",
"ct_granades",
"ct_weapon_awp",
"ct_eco",
"ct_force",
]
extra_columns = [
"pistol_round",
"round",
"round_winner",
"round_winner_t",
"round_winner_ct",
"t_health_thresholds",
"t_health_thresholds_num",
"ct_health_thresholds",
"ct_health_thresholds_num",
]
selected_columns = normal_columns + made_columns_ct + made_columns_t + extra_columns
# Getting only columns selected columns
final_df = treat_data[selected_columns]
final_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 122410 entries, 0 to 122409 Data columns (total 43 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 time_left 122410 non-null float64 1 ct_score 122410 non-null float64 2 t_score 122410 non-null float64 3 map 122410 non-null object 4 bomb_planted 122410 non-null bool 5 ct_health 122410 non-null float64 6 t_health 122410 non-null float64 7 ct_armor 122410 non-null float64 8 t_armor 122410 non-null float64 9 ct_money 122410 non-null float64 10 t_money 122410 non-null float64 11 ct_helmets 122410 non-null float64 12 t_helmets 122410 non-null float64 13 ct_defuse_kits 122410 non-null float64 14 ct_players_alive 122410 non-null float64 15 t_players_alive 122410 non-null float64 16 ct_main_rifle 122410 non-null float64 17 ct_sec_rifle 122410 non-null float64 18 ct_force_weapons 122410 non-null float64 19 ct_weak_pistols 122410 non-null float64 20 ct_strong_pistols 122410 non-null float64 21 ct_granades 122410 non-null float64 22 ct_weapon_awp 122410 non-null float64 23 ct_eco 122410 non-null int64 24 ct_force 122410 non-null int64 25 t_main_rifle 122410 non-null float64 26 t_sec_rifle 122410 non-null float64 27 t_force_weapons 122410 non-null float64 28 t_weak_pistols 122410 non-null float64 29 t_strong_pistols 122410 non-null float64 30 t_granades 122410 non-null float64 31 t_weapon_awp 122410 non-null float64 32 t_eco 122410 non-null int64 33 t_force 122410 non-null int64 34 pistol_round 122410 non-null int64 35 round 122410 non-null float64 36 round_winner 122410 non-null object 37 round_winner_t 122410 non-null int64 38 round_winner_ct 122410 non-null int64 39 t_health_thresholds 122410 non-null category 40 t_health_thresholds_num 122410 non-null category 41 ct_health_thresholds 122410 non-null category 42 ct_health_thresholds_num 122410 non-null category dtypes: bool(1), category(4), float64(29), int64(7), object(2) memory usage: 36.1+ MB
# checando nans
final_df.isna().sum()
time_left 0 ct_score 0 t_score 0 map 0 bomb_planted 0 ct_health 0 t_health 0 ct_armor 0 t_armor 0 ct_money 0 t_money 0 ct_helmets 0 t_helmets 0 ct_defuse_kits 0 ct_players_alive 0 t_players_alive 0 ct_main_rifle 0 ct_sec_rifle 0 ct_force_weapons 0 ct_weak_pistols 0 ct_strong_pistols 0 ct_granades 0 ct_weapon_awp 0 ct_eco 0 ct_force 0 t_main_rifle 0 t_sec_rifle 0 t_force_weapons 0 t_weak_pistols 0 t_strong_pistols 0 t_granades 0 t_weapon_awp 0 t_eco 0 t_force 0 pistol_round 0 round 0 round_winner 0 round_winner_t 0 round_winner_ct 0 t_health_thresholds 0 t_health_thresholds_num 0 ct_health_thresholds 0 ct_health_thresholds_num 0 dtype: int64
# Visualizando as correlações
plt.subplots(figsize=(24, 18))
sns.heatmap(final_df.corr(), annot=True)
plt.show()
total = final_df["round_winner"].count()
CT = (final_df.round_winner.value_counts().CT) / total
T = (len(final_df[final_df["round_winner"] == "T"])) / total
sizes = [T, CT]
labels = ["T", "CT"]
fig1, ax1 = plt.subplots()
ax1.set_title("Round Winners", color="white")
ax1.pie(sizes, labels=labels, autopct="%1.1f%%", shadow=True, startangle=90)
ax1.axis("equal")
plt.show()
score_per_map = (
final_df[final_df["map"] != "de_cache"]
.groupby(by="map")
.sum()[["round_winner_ct", "round_winner_t"]]
)
score_per_map["Contra Terrorista"] = (
100
* score_per_map["round_winner_ct"]
/ (score_per_map["round_winner_ct"] + score_per_map["round_winner_t"])
)
score_per_map["Terrorista"] = (
100
* score_per_map["round_winner_t"]
/ (score_per_map["round_winner_ct"] + score_per_map["round_winner_t"])
)
score_per_map["win_diff"] = (
score_per_map["Terrorista"] - score_per_map["Contra Terrorista"]
)
score_per_map
| round_winner_ct | round_winner_t | Contra Terrorista | Terrorista | win_diff | |
|---|---|---|---|---|---|
| map | |||||
| de_dust2 | 10158 | 11986 | 45.872471 | 54.127529 | 8.255058 |
| de_inferno | 10810 | 13001 | 45.399185 | 54.600815 | 9.201629 |
| de_mirage | 9144 | 9432 | 49.224806 | 50.775194 | 1.550388 |
| de_nuke | 10214 | 8811 | 53.687254 | 46.312746 | -7.374507 |
| de_overpass | 7026 | 7055 | 49.897024 | 50.102976 | 0.205951 |
| de_train | 7310 | 6181 | 54.184271 | 45.815729 | -8.368542 |
| de_vertigo | 5239 | 5898 | 47.041394 | 52.958606 | 5.917213 |
import plotly.express as px
df = px.data.tips()
fig = px.bar(
score_per_map,
x=score_per_map.index,
labels={
"value": "Rounds ganhos(%)",
"map": "Mapa",
"variable": "Lado",
},
y=["Contra Terrorista", "Terrorista"],
barmode="group",
)
fig.show()
time_slots = final_df.copy()
time_slots["time_left"] = pd.cut(time_slots["time_left"], 75)
time_slots["time_left"] = (
time_slots["time_left"].apply(lambda row: (row.left)).astype(int)
)
defuse_per_time = time_slots.groupby(by="time_left").sum()
defuse_per_time = defuse_per_time[defuse_per_time.index < 120]
df = px.data.tips()
fig = px.bar(
defuse_per_time,
x=defuse_per_time.index,
labels={"bomb_planted": "Bombas Plantadas", "time_left": "Tempo Restante"},
y="bomb_planted",
)
fig.show()
money_per_time = time_slots.groupby(by="time_left").mean()
money_per_time = money_per_time[money_per_time.index < 120]
money_per_time.index = money_per_time.index.astype("category")
df = px.data.tips()
fig = px.bar(
money_per_time,
x=money_per_time.index,
labels={
"value": "Vida do time",
"time_left": "Tempo Restante",
"variable": "Lado",
},
y=["ct_health", "t_health"],
barmode="group",
)
fig.show()